{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Pynamical Stock Predictions"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:29:54.944Z",
          "iopub.execute_input": "2020-06-07T02:29:54.948Z",
          "iopub.status.idle": "2020-06-07T02:29:56.052Z",
          "shell.execute_reply": "2020-06-07T02:29:56.058Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock = 'AAPL'\n",
        "start = '2015-01-01' \n",
        "end = '2020-01-01'\n",
        "df = yf.download(stock, start, end)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:30:10.263Z",
          "iopub.execute_input": "2020-06-07T02:30:10.272Z",
          "iopub.status.idle": "2020-06-07T02:30:11.730Z",
          "shell.execute_reply": "2020-06-07T02:30:11.741Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>99.945885</td>\n",
              "      <td>109.330002</td>\n",
              "      <td>111.440002</td>\n",
              "      <td>107.349998</td>\n",
              "      <td>111.389999</td>\n",
              "      <td>53204600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>97.130241</td>\n",
              "      <td>106.250000</td>\n",
              "      <td>108.650002</td>\n",
              "      <td>105.410004</td>\n",
              "      <td>108.290001</td>\n",
              "      <td>64285500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>97.139420</td>\n",
              "      <td>106.260002</td>\n",
              "      <td>107.430000</td>\n",
              "      <td>104.629997</td>\n",
              "      <td>106.540001</td>\n",
              "      <td>65797100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07</th>\n",
              "      <td>98.501518</td>\n",
              "      <td>107.750000</td>\n",
              "      <td>108.199997</td>\n",
              "      <td>106.699997</td>\n",
              "      <td>107.199997</td>\n",
              "      <td>40105900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08</th>\n",
              "      <td>102.286186</td>\n",
              "      <td>111.889999</td>\n",
              "      <td>112.150002</td>\n",
              "      <td>108.699997</td>\n",
              "      <td>109.230003</td>\n",
              "      <td>59364500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Adj Close       Close        High         Low        Open  \\\n",
              "Date                                                                     \n",
              "2015-01-02   99.945885  109.330002  111.440002  107.349998  111.389999   \n",
              "2015-01-05   97.130241  106.250000  108.650002  105.410004  108.290001   \n",
              "2015-01-06   97.139420  106.260002  107.430000  104.629997  106.540001   \n",
              "2015-01-07   98.501518  107.750000  108.199997  106.699997  107.199997   \n",
              "2015-01-08  102.286186  111.889999  112.150002  108.699997  109.230003   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2015-01-02  53204600  \n",
              "2015-01-05  64285500  \n",
              "2015-01-06  65797100  \n",
              "2015-01-07  40105900  \n",
              "2015-01-08  59364500  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:30:30.589Z",
          "iopub.execute_input": "2020-06-07T02:30:30.593Z",
          "iopub.status.idle": "2020-06-07T02:30:30.626Z",
          "shell.execute_reply": "2020-06-07T02:30:30.675Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>282.831299</td>\n",
              "      <td>284.269989</td>\n",
              "      <td>284.890015</td>\n",
              "      <td>282.920013</td>\n",
              "      <td>284.690002</td>\n",
              "      <td>12119700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-26</th>\n",
              "      <td>288.442780</td>\n",
              "      <td>289.910004</td>\n",
              "      <td>289.980011</td>\n",
              "      <td>284.700012</td>\n",
              "      <td>284.820007</td>\n",
              "      <td>23280300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-27</th>\n",
              "      <td>288.333313</td>\n",
              "      <td>289.799988</td>\n",
              "      <td>293.970001</td>\n",
              "      <td>288.119995</td>\n",
              "      <td>291.119995</td>\n",
              "      <td>36566500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-30</th>\n",
              "      <td>290.044617</td>\n",
              "      <td>291.519989</td>\n",
              "      <td>292.690002</td>\n",
              "      <td>285.220001</td>\n",
              "      <td>289.459991</td>\n",
              "      <td>36028600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>292.163818</td>\n",
              "      <td>293.649994</td>\n",
              "      <td>293.679993</td>\n",
              "      <td>289.519989</td>\n",
              "      <td>289.929993</td>\n",
              "      <td>25201400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Adj Close       Close        High         Low        Open  \\\n",
              "Date                                                                     \n",
              "2019-12-24  282.831299  284.269989  284.890015  282.920013  284.690002   \n",
              "2019-12-26  288.442780  289.910004  289.980011  284.700012  284.820007   \n",
              "2019-12-27  288.333313  289.799988  293.970001  288.119995  291.119995   \n",
              "2019-12-30  290.044617  291.519989  292.690002  285.220001  289.459991   \n",
              "2019-12-31  292.163818  293.649994  293.679993  289.519989  289.929993   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2019-12-24  12119700  \n",
              "2019-12-26  23280300  \n",
              "2019-12-27  36566500  \n",
              "2019-12-30  36028600  \n",
              "2019-12-31  25201400  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:30:33.082Z",
          "iopub.execute_input": "2020-06-07T02:30:33.087Z",
          "iopub.status.idle": "2020-06-07T02:30:33.098Z",
          "shell.execute_reply": "2020-06-07T02:30:33.102Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:45:00.749Z",
          "iopub.execute_input": "2020-06-07T02:45:00.754Z",
          "iopub.status.idle": "2020-06-07T02:45:00.762Z",
          "shell.execute_reply": "2020-06-07T02:45:00.765Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def f(x):\n",
        "    return  np.log(x / x.shift(1))"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:45:18.179Z",
          "iopub.execute_input": "2020-06-07T02:45:18.183Z",
          "iopub.status.idle": "2020-06-07T02:45:18.190Z",
          "shell.execute_reply": "2020-06-07T02:45:18.193Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rets = df['Adj Close'].pct_change().dropna()"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:46:13.940Z",
          "iopub.execute_input": "2020-06-07T02:46:13.945Z",
          "iopub.status.idle": "2020-06-07T02:46:13.953Z",
          "shell.execute_reply": "2020-06-07T02:46:13.961Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = (rets + 1).cumprod()"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T02:49:15.228Z",
          "iopub.execute_input": "2020-06-07T02:49:15.234Z",
          "iopub.status.idle": "2020-06-07T02:49:15.241Z",
          "shell.execute_reply": "2020-06-07T02:49:15.249Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x.values"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": [
              "array([0.97182832, 0.97192015, 0.98554851, ..., 2.8848943 , 2.9020166 ,\n",
              "       2.92322009])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T03:15:20.055Z",
          "iopub.execute_input": "2020-06-07T03:15:20.060Z",
          "iopub.status.idle": "2020-06-07T03:15:20.069Z",
          "shell.execute_reply": "2020-06-07T03:15:20.074Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x.min()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": [
              "0.8500891633938563"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 46,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T03:20:58.522Z",
          "iopub.execute_input": "2020-06-07T03:20:58.527Z",
          "iopub.status.idle": "2020-06-07T03:20:58.542Z",
          "shell.execute_reply": "2020-06-07T03:20:58.546Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x.max()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": [
              "2.923220092782452"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 47,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T03:21:00.483Z",
          "iopub.execute_input": "2020-06-07T03:21:00.487Z",
          "iopub.status.idle": "2020-06-07T03:21:00.497Z",
          "shell.execute_reply": "2020-06-07T03:21:00.500Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pops = simulate(num_gens=100, rate_min=x.min(), rate_max=3, num_rates=1000)"
      ],
      "outputs": [],
      "execution_count": 48,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T03:21:07.473Z",
          "iopub.execute_input": "2020-06-07T03:21:07.480Z",
          "iopub.status.idle": "2020-06-07T03:21:07.792Z",
          "shell.execute_reply": "2020-06-07T03:21:07.801Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pynamical import logistic_map, simulate, bifurcation_plot\n",
        "bifurcation_plot(pops,xlabel=\"Time\", ylabel=\"Stock Growth\")"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              "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\n"
            ],
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 49,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-07T03:21:11.327Z",
          "iopub.execute_input": "2020-06-07T03:21:11.331Z",
          "iopub.status.idle": "2020-06-07T03:21:17.028Z",
          "shell.execute_reply": "2020-06-07T03:21:17.041Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.5.5",
      "nbconvert_exporter": "python",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:\\Users\\Tin Hang\\Anaconda3\\envs\\py35\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.23.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}